The premise of this allocation is a simple question: if one were to buy real estate in a handful of cities (rather in every single market), where would the properties be located in order to diversify portfolio risk? We used long time series of monthly data for this analysis (15 years), which is quite unusual for us. But there hasn’t been a drawdown in real estate prices for such a long time that having a risk-based approach is almost nonsensical using shorter series.

Symbol City Name Target Allocation (%)
LXXRSA S&P/Case-Shiller CA-Los Angeles Home Price Index 16.98182
CHXRSA S&P/Case-Shiller IL-Chicago Home Price Index 16.57417
WDXRSA S&P/Case-Shiller DC-Washington Home Price Index 27.46054
MNXRSA S&P/Case-Shiller MN-Minneapolis Home Price Index 18.48161
CRXRNSA S&P/Case-Shiller NC-Charlotte Home Price Index 20.50186

» Comparison of our allocation versus the 20-City Composite Index over the last fifteen years:

 

 

                    Note: The benchmark (S&P/Case-Shiller 20-City Composite Home Price Index) has been scaled to achieve equal volatility over the period, in order to get a pound for pound visual comparison of compounded returns.

 

» Key performance metrics over the last fifteen years:

 

Annualized Return Annualized Volatility Relative Volatility / Benchmark Sharpe Ratio Maximum Drawdown1 Calmar Ratio Correlation Beta R-squared2 Annualized Alpha Treynor Ratio Up Capture Down Capture Active Return3 Tracking Error3 Information Ratio3
ALLOCATION 5.0% 1.8% 0.6 2.76 -7.3% 0.68 0.8 0.48 65% 2.1% 0.1 67.5% 7.6% 1.4% 1.1% 1.24
BENCHMARK 5.9% 3.0% – 1.97 -9.9% 0.59 – – – – – – – – – –
LXXRSA 6.5% 2.5% 0.8 2.59 -7.4% 0.87 0.62 0.52 38% 3.4% 0.12 83.2% -6.7% 1.5% 2.2% 0.67
CHXRSA 3.8% 2.3% 0.8 1.69 -13.7% 0.28 0.59 0.44 35% 1.2% 0.09 54.1% 14.6% -0.6% 2.1% -0.29
WDXRSA 4.2% 1.7% 0.6 2.49 -3.1% 1.34 0.56 0.31 31% 2.3% 0.13 50.5% -21.9% 0.9% 1.6% 0.54
MNXRSA 4.6% 2.3% 0.8 2 -12.4% 0.37 0.5 0.38 25% 2.3% 0.12 60.8% 1.1% 0.1% 2.3% 0.02
CRXRNSA 6.1% 2.8% 0.9 2.16 -7.6% 0.81 0.86 0.81 73% 1.3% 0.08 94.0% 59.0% 0.5% 1.5% 0.34
Notes:
1 Drawdowns are calculated on monthly prices (the frequency of the data).
2 The R-squared indicates how much the variance of a security can be explained by the variance of the benchmark: if a security has a low R-squared value, the CAPM single-factor linear relationship doesn’t explain much of the variance of the security, and the CAPM metrics (beta, alpha, Treynor ratio) are not useful for this particular security.
3 The active performance metrics are calculated relative to the volatilty-adjusted benchmark (i.e. the benchmark multiplied by the relative volatility of the allocation).

» Long-term charts for each city in the allocation:

 











Check out some of our other allocations and please get in touch if you have any comments or questions.
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